The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

Table of Contents


The Covid-19 epidemic is affecting all areas of life, including the training activities of universities around the world. Therefore, the online learning method is an effective method in the present time and is used by many universities. However, not all training institutions have sufficient conditions, resources, and experience to carry out online learning, especially in under-resourced developing countries. Therefore, the construction of traditional courses (face to face), e-learning, or blended learning in limited conditions that still meet the needs of students is a problem faced by many universities today. To solve this problem, we propose a method of evaluating the influence of these factors on the e-learning system. From there, it is a matter of clarifying the importance and prioritizing construction investment for each factor based on the K-means clustering algorithm, using the data of students who have been participating in the system. At the same time, we propose a model to support students to choose one of the learning methods, such as traditional, e-learning or blended learning, which is suitable for their skills and abilities. The data classification method with the algorithms multilayer perceptron (MP), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) and naïve bayes (NB) is applied to find the model fit. The experiment was conducted on 679 data samples collected from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam. With our proposed method, the results are obtained from experimentation for the different effects of infrastructure, teachers, and courses, also as features of these factors. At the same time, the accuracy of the prediction results which help students to choose an appropriate learning method is up to 81.52%.


factors affecting, e-learning system, machine learning for e-learning


The Covid-19 pandemic is spreading rapidly around the world and shows no signs of stopping. With different infection modes, there were nearly 20 million infections and more than 0.73 million deaths at the time of writing [1]. Moreover, the epidemic has affected all fields of economics and politics, and the social life of all people around the world [2]. The social separation and isolation required to avoid the spread of the epidemic has formed new habits and activities of life through the internet. Education is one of the fields greatly influenced as it is necessary for many people and in each different country. Students are limited in their ability to go to school and to participate in collective activities, so learning, research, and exchange activities are mainly done according to the e-learning or blended learning methods [3,4]. This approach has provided universities with an effective method of training in combining e-learning with traditional training [4], depending on the content and resources that the universities can provide for courses of e-learning at different levels. Favorable features for educational activities include online interactive activities, and data storage and assessment, which can be conducted on an internet platform so that students and faculty members can complete their tasks without being face to face [3,5].

However, e-learning has limitations: teachers and students must have certain skills, knowledge and experience in technology and pedagogical skills to complete their course. The infrastructure of the e-learning system needs to be synchronous, efficient, and secure. This is required to support teacher and student interaction, to store data, and evaluate the effectiveness of the course [6]. Therefore, the problem of how to have a good e-learning system is established. One of the interesting approaches is evaluating systems based on the influencing factors [7,8].

Katerina Kabassi et al. [9] evaluated the learning management system for blended learning in Greek using six influencing factors: platform, student, teacher, design, courses, environment and technology. Then, they used statistical methods to evaluate and analyze their system. Cheol-Rim Choi et al. [10] assessed the quality of the multimedia content of the e-learning system using four groups of factors: system quality, information quality, service quality, and the ability to evaluate the system based on calculating the weight of attributes using the analytic network process (ANP) technique. However, the weight initialized for attributes needs to be calculated carefully because it affects the evaluation results. Said A. Salloum et al. [5] used four groups of factors affecting the efficiency of e-learning systems: technology innovativeness, knowledge sharing, quality and trust. The data samples were collected from 251 students and statistical methods were used to evaluate the system.

Therefore, the method of sampling data from students in e-learning using affecting factors is scientifically grounded. In this research, we propose a method of evaluating e-learning systems based on data gathered from feedback of students using an explicit factor model (EFM) with clustering algorithm. We suggest separating factors into four groups such as: student, teacher, infrastructure and course, including sixteen features. The data clustering method used was the K-means machine learning algorithm. It is used to divide data into clusters. Thereby, it could help managers to evaluate and compare factors affecting the system to make appropriate decisions and build a better system.

The e-learning and blended learning systems are used by universities, and they are providing students with both online and traditional learning methods. Courses may be in online, traditional, or blended form depending on each institution. Normally, students often need help to get information on what form is suitable for their skills and abilities. This is solved by assistants or professors. However, this requires a lot of resources and sometimes does not really work out well. One interesting approach is to use data mining methods [11,12] which, from the available data, use an analytic process to give information about a problem in the future.

With the development of machine learning techniques and artificial intelligence, we propose a method to help students choose an appropriate learning method based on classification algorithms. It is called the studying method selection (SMS) model. According to this method, training data has been labelled and collected from previous students. Using the analytical SMS model, learners will receive results that they could use to select the best form of study. The most important aspect of this approach is how to show a set of features for systems. It is also a concerning problem for researchers, as in the following cases:

Alan Y.K. Chan et al. [8] evaluated online courses based on four main factors: online courses, learning effectiveness, evaluation methods and evaluation results. The student interaction information from the system based on the above factors is stored in the database. It is applied to system analysis and evaluation. The evaluation is based on the statistical method, aiming to recognize interactions between system factors. Said A. Salloum et al. [5] surveyed more than 280 students with a behavior intention model to predict the students’ intention to use e-learning with five related hypotheses. Sujit Kumar Basa et al. [13] have identified the main elements of e-learning to provide a framework to assess the uneven and difficult implementation of e-learning in initial and continuing educational institutions. Thereby, they are providing information to both learners and administrators. Their research is based on eight factors: institutional, technical, resource, training, competency, infrastructural, attitudinal and social integration. F. Martin et al. [8] have built a framework to evaluate the quality of an online course with seven factors: institutional support, technology infrastructure, course design, learner and instructor support, course assessment and evaluation, learning effectiveness, faculty and student satisfaction.

The training system is also built by effective learners. Therefore, feedback and assessment of students about the training system and capacity of the institution are useful. It helps to plan the policy and improve the quality of system, and following the above researches, identify influence factors based on the system purpose. They also affect the results, methodology and the type of evaluation. Because of that, we suggest four factors including sixteen features, which affect e-learning systems. Based on the features, a classification model will be made for forecasting the learning method. It helps the students choose a suitable method with their abilities and conditions. In summary, we suggest four affected factors including sixteen features. Base on that, data samples that are collected from students who have taken the courses aim to solve two different problems: firstly, to evaluate the influence measurement of factors and features based on the clustering method by the K-mean algorithm. Secondly, to build a prediction model using classification techniques to support the students in choosing an appropriate learning method. The experiment is on the collected data samples from 303 students studying at the Academy of Journalism and Communication (AJC), Vietnam, with the prediction result being up to 81.52% accurate from the studying method selection model (SMSM). It is meaningful for AJC to enhance training qualities. Specially, to provide training in journalistic, publication, and political courses at any location of the nation.

Conclusions and Future Work

Building a strategy, operating, and managing online learning systems are some of the most important problems of each university. Choosing and properly assessing the influence of factors will help managers to have appropriate strategies. In our research, we have proposed four influencing factors including sixteen features, using data clustering algorithms to analyze elements and features to help managers enhance training qualities and systems effectively. At the same time, we used data classification techniques to build a model to support students in choosing the learning methods, such as e-learning, traditional, or blended leaning, that are suitable for their course content and learning capacity from the datasets of previous learners with accuracy of result of up to 81.52%. The research is still limited because the experimental data has only been from AJC. The classification algorithms have used default parameters. Therefore, in the next studies, we will add more features, use the evaluation methods of feature selection, use advanced techniques such as deep learning and perform the steps of optimization of parameters and algorithms for higher results. Beside the limitations, the research has proposed a set of features to enhance effective e-learning in order to build and improve the quality of the education system by current popular techniques. At the same time, we proposed research on facilities to improve the performance of training institutions based on successful application of artificial intelligence.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.

The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.

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The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach



Dang-Nhac Lu , Hong-Quang Le ,Tuan-Ha Vu




The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach

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education Sciences, v10 Article 270 2020


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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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